Parameter estimation

colfi.nde

class colfi.nde.NDEs(obs_data, model, param_names, params_dict=None, cov_matrix=None, init_chain=None, init_params=None, nde_type='MNN', num_train=3000, num_vali=100, base_N=1000, space_type='hyperellipsoid', local_samples=None, chain_n=3, chain_leng=10000)

Bases: PlotPosterior

Estimating (cosmological) parameters with Neural Density Estimators (NDEs).

Parameters:

• obs_data (array-like or list) – The observations (measurements) with shape (obs_length,3), or a list of observations with shape [(obs_length_1,3), (obs_length_2,3), …]. The first column is the observational variable, the second column is the best values of the measurement, and the third column is the error of the measurement.

• model (cosmological (or theoretical) model instance) – A cosmological (or theoretical) model instance that is used to simulate training set, it should contains a ‘simulate’ method, and ‘simulate’ should accept input of cosmological parameters, if you use local data sets, it should also contain ‘load_params’ and ‘load_sample’ methods.

• param_names (list) – A list which contains the parameter names, e.g. [‘H0’,’ombh2’,’omch2’].

• params_dict (dict or None, optional) – Information of cosmological parameters that include the labels, the minimum values, and the maximum values. See params_dict_zoo(). Default: None

• cov_matrix (array-like, list, or None, optional) – Covariance matrix of the observational data. It should be an array with shape (obs_length, obs_length), or a list of covariance matrix with shape [(obs_length_1, obs_length_1), (obs_length_2, obs_length_2), …]. If there is no covariance for some observations, the covariance matrix should be set to None. e.g. [cov_matrix_1, None, cov_matrix_3]. Default: None

• init_chain (None or array-like, optional) – The initial ANN or MCMC chain, which is usually based on prvious parameter estimation. Default: None

• init_params (None or array-like, optional) – The initial settings of the parameter space. If init_chain is given, init_params will be ignored. Default: None

• nde_type (str or list, optional) – A string (or a list with two strings in it) that indicate which NDE should be used. There are four NDEs that can be used: ‘ANN’, ‘MDN’, ‘MNN’, or ‘ANNMC’. If a string is given, such as ‘ANN’, only ANN will be used for parameter estimation. If a list that contains two NDEs is given, such as [‘ANN’, ‘MNN’], then ANN will be used in the burn-in phase to find the burn-in end step, MNN will be used after the burn-in phase to obtain the posterior. Default: ‘MNN’

• num_train (int, optional) – The number of samples of the training set. Default: 3000

• num_vali (int, optional) – The number of samples of the validation set. Default: 100

• base_N (int, optional) – The basic (or minimum) number of samples in the training set, which works only in the burn-in phase. Default: 1000

• space_type (str, optional) – The type of parameter space. It can be ‘hypercube’, ‘LHS’, ‘hypersphere’, ‘hyperellipsoid’, or ‘posterior_hyperellipsoid’. Default: ‘hyperellipsoid’

• local_samples (None, str, or list, optional) – Path of local samples, None, or ‘sample’ or [‘sample’] or [‘sample_1’, ‘sample_2’, …]. If None, no local samples are used. Default: None

• chain_n (int, optional) – If the number of ANN chains to be obtained, which also equals to the steps after the burn-in phase, it will be used to stop the whole training process. This only works after burn-in phase. Default: 3

• chain_leng (int, optional) – The length of each ANN chain. Default: 10000

Variables:

• activation_func (str, optional) – The name of activation function, which can be ‘ReLU’, ‘LeakyReLU’, ‘PReLU’, ‘RReLU’, ‘ReLU6’, ‘ELU’, ‘CELU’, ‘SELU’, ‘SiLU’, ‘Sigmoid’, ‘LogSigmoid’, ‘Tanh’, ‘Tanhshrink’, ‘Softsign’, or ‘Softplus’ (see activation()). Default: ‘Softplus’

• hidden_layer (int, optional) – The number of the hidden layer of the network (for a single branch network). Default: 3

• branch_hiddenLayer (int, optional) – The number of the hidden layer for the branch part of the network (for a multibranch network). Default: 1

• trunk_hiddenLayer (int, optional) – The number of the hidden layer for the trunk part of the network (for a multibranch network). Default: 2

• lr (float, optional) – The learning rate setting of the network. Default: 1e-2

• lr_min (float, optional) – The minimum of the learning rate. Default: 1e-8

• batch_size (int, optional) – The batch size setting of the network. Default: 1250

• auto_batchSize (bool, optional) – If True, the batch size will be set automatically in the training process, otherwise, use the setting of batch_size. Default: True

• epoch (int, optional) – The number of epoch of the training process. Default: 2000

• epoch_branch (int, optional) – The number of epoch of the training process (for the branch part of the multibranch network). Default: 2000

• auto_epoch (bool, optional) – If True, the epoch will be set automatically in the training process, otherwise, use the setting of epoch. Default: False

• comp_type (str, optional) – The name of component used in the MDN method, which can be ‘Gaussian’ or ‘Beta’. Default: ‘Gaussian’

• comp_n (int, optional) – The number of components used in the MDN method. Default: 3

• spaceSigma (int or array-like, optional) – The size of parameter space to be learned. It is a int or a numpy array with shape of (n,), where n is the number of parameters, e.g. for spaceSigma=5, the parameter space to be learned is \([-5\sigma, +5\sigma]\). Default: 5

• noise_type (str, optional) – The type of Gaussian noise added to the training set, which should be ‘singleNormal’ or ‘multiNormal’. It only works for the NDEs ANN, MDN, and MNN. For ANN and MNN, both ‘singleNormal’ and ‘multiNormal’ can be used, but it is recommended to use ‘multiNormal’; For MDN, only ‘singleNormal’ can be used. Default: ‘multiNormal’

• factor_sigma (float, optional) – For the case of noise_type = ‘singleNormal’, factor_sigma should be set to 1. For the case of noise_type = ‘multiNormal’, it is the standard deviation of the coefficient of the observational error (standard deviation). Default: 0.2

• multi_noise (int, optional) – The number of realization of noise added to the measurement in one epoch. Default: 5

• scale_obs (bool, optional) – If True, the observational data (measurements) will be scaled based on the base values of the data. Default: False

• scale_params (bool, optional) – If True, the cosmological parameters will be scaled based on the base values of parameters. See ParamsScaling. Default: True

• norm_obs (bool, optional) – If True, the observational data feed to the network will be normalized. Default: True

• norm_params (bool, optional) – If True, the cosmological parameters will be normalized. Default: True

• independent_norm_obs (bool, optional) – If True, each data point in the observational data (measurements) will be normalized independently. Default: False

• independent_norm_params (bool, optional) – If True, each cosmological parameters will be normalized independently. Default: True

• norm_type (str, optional) – The method of normalization, which can be ‘z_score’, ‘minmax’, or ‘mean’ (see Normalize). Default: ‘z_score’

• train_branch (bool, optional) – If True, the branch part of the multibranch network will be trained before training the entire network. Default: False

• repeat_n (int, optional) – The number of iterations using the same batch of data during network training, which is usually set to 1 or 3. Default: 3

• fast_training (bool, optional) – If True, the batch size will be set to batch_size*multi_noise and the network will be trained fast. Default: False

• randn_num (float or str, optional) – A random number that identifies the saved results. Default: float

• file_identity (str, optional) – A string that identifies the files saved to the disk, which is useful to identify the saved files. Default: ‘’

• expectedBurnInEnd_step (int, optional) – The expected burn-in end step. If the burn-in phase does not end at a step equal to expectedBurnInEnd_step, the training process will be broken, which means the setting of hyperparameters is not good or the NDE used is not suitable.

• chain_true_path (str, optional) – The path of the true chain of the posterior which can be obtained by using other methods, such as the MCMC method. Note: only .npy and .txt file is supported. Default: ‘’

• label_true (str, optional) – The legend label of the true chain. Default: ‘True’

• fiducial_params (list, optional) – A list that contains the fiducial cosmological parameters. Default: []

Note

The number of samples of the training set should be large enough to ensure the network learns a reliable mapping. For example, set num_train to 1000, or a larger value like 3000.

The epoch should also be set large enough to ensure a well-learned network. e.g. set epoch to 2000, or a larger value like 3000.

The initial parameter space is suggested to set large enough to cover the true parameters. In this case, it be easier for the network to find the best-fit value of parameters.

It is better to set the number of ANN chains chain_n a large value like 3, and this will minimize the effect of randomness on the results. However, it is also acceptable to set a smaller value like 1.

The advantage of this method is that we can analyze the results before the end of the training process, and determine how many steps can be used to estimate parameters.

Local samples can be used as training set to save time, so when using this method, you can generate a sample library for later reuse.

property base_epoch

property chain

Combined ANN chain using the result of steps after burn-in.

property cov_copy

property file_identity_str

get_space_type_parts()

property good_chains

The ANN chians after the burn-in phase.

property good_losses

property num_train_burnIn

property obs_errors

property obs_variables

print_hparams()

save_ndeInfo(path='ann', randn_num=0.123)

save_variables(randn_num=0.123)

simulate(nde_type, space_type, step, burnInEnd, param_devs, error_devs, spaceSigma_all, space_type_all=[], prev_space=None, chain_all=[], sim_obs=None, sim_params=None)

Simulate training data.

split_data(sim_obs, sim_params, burnInEnd=False)

Split the simulated data into training set and validation set.

train(path='ann', save_items=True, showEpoch_n=100)

Train NDEs and save the results.

Parameters:

• path (str, optional) – The path of the results to be saved. Default: ‘ann’

• save_items (bool, optional) – If True, results will be saved to disk, otherwise, results will not be saved.

• showEpoch_n (int, optional) – The number of iterations interval for printing. Default: 100

Returns:

A list of chains and losses.

Return type:

list

update_params_space(step, chain_all, burnInEnd, burnInEnd_step)

class colfi.nde.Predict(obs_data=None, cov_matrix=None, path='ann', randn_num=<class 'float'>, steps_n=None)

Bases: PlotPosterior

Reanalysis using the saved chains or the well-trained NDEs.

Parameters:

• obs_data (array-like, list, or None, optional) – The observations (measurements) with shape (obs_length,3), or a list of observations with shape [(obs_length_1,3), (obs_length_2,3), …]. The first column is the observational variable, the second column is the best values of the measurement, and the third column is the error of the measurement. If None, only the saved chains can be used for parameter estimations, and will not check variables. Default: None

• cov_matrix (array-like, list, or None, optional) – Covariance matrix of the observational data. It should be an array with shape (obs_length, obs_length), or a list of covariance matrix with shape [(obs_length_1, obs_length_1), (obs_length_2, obs_length_2), …]. If there is no covariance for some observations, the covariance matrix should be set to None. e.g. [cov_matrix_1, None, cov_matrix_3]. Default: None

• path (str, optional) – The path of the results saved. Default: ‘ann’

• randn_num (float or str, optional) – A random number that identifies the saved results. Default: float

• steps_n (None or int, optional) – The number of steps of the training process to be used. If None, the files will be found automatically. Default: None

Variables:

• chain_leng (int, optional) – The length of each ANN chain, which equals the number of samples to be generated by a NDE model when predicting an ANN chain. This only works when using the from_net() method. Default: 10000

• chain_true_path (str, optional) – The path of the true chain of the posterior which can be obtained by using other methods, such as the MCMC method. Note: only .npy and .txt file is supported. Default: ‘’

• label_true (str, optional) – The legend label of the true chain. Default: ‘True’

• fiducial_params (list, optional) – A list that contains the fiducial cosmological parameters. Default: []

• show_idx (None or list, optional) – The index of cosmological parameters when plotting contours. This allows us to change the order of the cosmological parameters. If None, the order of parameters follows that in the ANN chain. If list, the minimum value of it should be 1. See PlotPosterior. Default: None

property cov_copy

from_chain()

Predict using saved chains.

Raises:

ValueError – If variables of the input observational data are different from those used to train the NDE, an error will be raised.

from_net()

Predict using saved NDEs.

Raises:

ValueError – If variables of the input observational data are different from those used to train the NDE, an error will be raised.

get_eco()

get_loss(alpha=0.6, show_logLoss=False, save_fig=True, show_minLoss=True)

property obs_variables

property same_variables

property trained_variables

class colfi.nde.PredictNDEs(path='ann', randn_nums=[1.123, 1.123])

Bases: PlotMultiPosterior

Reanalysis using the saved chains for several NDE results.

Parameters:

• path (str, optional) – The path of the results saved. Default: ‘ann’

• randn_nums (list, optional) – A series of random numbers identify the saved results. Default: [1.123,1.123]

Variables:

• chain_true_path (str, optional) – The path of the true chain of the posterior which can be obtained by using other methods, such as the MCMC method. Note: only .npy and .txt file is supported. Default: ‘’

• label_true (str, optional) – The legend label of the true chain. Default: ‘True’

• show_idx (None or list, optional) – The index of cosmological parameters when plotting contours. This allows us to change the order of the cosmological parameters. If None, the order of parameters follows that in the ANN chain. If list, the minimum value of it should be 1. See PlotPosterior. Default: None

from_chain()

Predict using saved chains.

colfi.space_updater

class colfi.space_updater.Chains

Bases: object

static bestFit(chain, best_type='mode', out_sigma=1, symmetry_error=True, bins=100, smooth=5)

Get the best-fit parameters from the chain.

Parameters:

• chain (array-like) – The ANN chain.

• best_type (str, optional) – The type of the best values of parameters, ‘mode’ or ‘median’. If ‘mode’, it will take the mode as the best value. If ‘median’, it will take the median as the best value. Default: ‘mode’

• out_sigma (int) – The output sigma, which can be 1, 2, or 3. Default: 1

• symmetry_error (bool, optional) – If True, obtain symmetrical errors, otherwise, obtain unsymmetrical errors. Default: True

static best_median(chain)

Take the median as the best value.

static best_mode(chain, bins=100, smooth=5)

Take the mode as the best value.

static cov_matrix(chain, max_error=True, expand_factor=0)

Get the covariance matrix.

Parameters:

• chain (array-like) – The ANN chain.

• max_error (bool, optional) – If True, the diagonal elements of the covariance matrix will be replaced by the estimated maximum errors, which is useful for non-Gaussian distribution. If it is set to False, the obtained covariance matrix may be non-positive definite for some cases, so it is recommended to put it to True. Default: True

• expand_factor (float, optional) – The expansion factor that is used to expand the error (the standard deviation) of each cosmological parameter. For example, if expand_factor=0.05, the error will has 5% expansion. It only works when max_error is True. Default: 0

Returns:

cov – The covariance matrix.

Return type:

array-like

static error_devs(chain_1, chain_true)

Get the absolute values of the relative deviations of error of parameters obtained from two chains.

static param_devs(chain_1, chain_2)

Get deviations of parameters obtained from two chains.

static params_n(chain)

static reshape_chain(chain)

static sigma(chain, best_values, out_sigma=1)

Calculate the standard deviations.

Parameters:

• chain (array-like) – The ANN chain.

• best_values (1-dimension array) – The best values of parameters.

• out_sigma (int) – The output sigma, which can be 1, 2, or 3. Default: 1

Returns:

• sigma_1l, sigma_2l, sigma_3l (1-dimension array) – The left 1 sigma, 2 sigma, or 3sigma deviations.

• sigma_1r, sigma_2r, sigma_3r (1-dimension array) – The right 1 sigma, 2 sigma, or 3sigma deviations.

class colfi.space_updater.CheckParameterSpace

Bases: object

static check_limit(p_space, limit_space)

Check the parameter space to ensure that the parameter space does not exceed its limit range.

Parameters:

• p_space (array-like) – The parameter space to be checked.

• limit_space (array-like) – The limit range of parameter space.

Returns:

A parameter space being limited by its limit range.

Return type:

array-like

class colfi.space_updater.UpdateParameterSpace(step, param_names, chain_1, chain_0=None, init_params=None, spaceSigma=5, params_dict=None)

Bases: CheckParameterSpace

Update parameter space.

Parameters:

• step (int) – The number of step in the training process.

• param_names (list) – A list that contains parameter names.

• chain_1 (array-like) – The ANN chain of the i-th step, where \(i\geq2\).

• chain_0 (None or array-like, optional) – The ANN chain of the (i-1)-th step, where \(i\geq2\), if step \(\leq2\), chain_0 should be set to None, otherwise, chain_0 should be an array. Default: None

• init_params (None or array-like) – The initial settings of the parameter space. If chain_0 is given, init_params will be ignored. Default: None

• spaceSigma (int or array-like, optional) – The size of parameter space to be learned. It is a int or a numpy array with shape of (n,), where n is the number of parameters, e.g. for spaceSigma=5, the parameter space to be learned is \([-5\sigma, +5\sigma]\). Default: 5

• params_dict (dict or None, optional) – Information of cosmological parameters that include the labels, the minimum values, and the maximum values. See params_dict_zoo(). Default: None

property error_devs

limited_spaceSigma_all()

property param_devs

params_space()

Obtain the parameter space to be learned from chain.

Returns:

Limited parameter space.

Return type:

array-like

print_learningRange()

small_dev(limit_dev=0.01)

A small value of deviation of parameters between two steps used to end the training process.

Parameters:

limit_dev (float, optional) – If the deviation of parameters between two steps smaller than this value, the training process will be over. Default: 0.01 (the deviation < 1%)

Returns:

True (dev \(\leq\) limit_dev) or False (dev>limit_dev)

Return type:

bool

property spaceSigma_all

property spaceSigma_max

property spaceSigma_min

colfi.space_updater.get_CovMatrix(chain, params_n, best_values=None)

Calculate covariance matrix from a chain.

Parameters:

• chain (array-like) – An ANN or MCMC chain with shape (N, M), where N is the number of chain and M is the number of parameters.

• params_n (array-like) – The number of parameters.

• best_values (array-like) – The best-fit values.

Returns:

cov_matrix – Covariance matrix.

Return type:

array-like

colfi.space_updater.get_cov(X, Y, mean=None)

Calculate covariance or variance.

Parameters:

• X (array-like) – Random variable X.

• Y (array-like) – Random variable Y.

• mean (array-like or list, optional) – The mean values of X and Y. Default: None

Returns:

Covariance or variance.

Return type:

float

colfi.space_updater.pdf_1(X, bins=100, smooth=5)

Estimate the probability density function for the given data.

colfi.plotter

class colfi.plotter.BestFitsData(chain_all, chain, param_labels='', burnInEnd_step=None, nde_type_pair=['ANN', 'MNN'], show_initParams=False, init_params=<class 'float'>, chain_true=None, label_true='True', show_idx=None)

Bases: object

Best fit values of each steps, used to plot steps in PlotPosterior

property bestFits_all

property best_fit

property best_fit_true

panel(data)

panel_data(p_index)

panels_data()

class colfi.plotter.BestPredictedData(params_testSet, predParams_testSet, params_trainingSet=None, predParams_trainingSet=None, param_labels='', show_reErr=True, coef_type='R2')

Bases: object

panel(data)

panel_data(p_index)

panels_data()

class colfi.plotter.LossesData(good_losses, alpha=0.6, title_labels='', text_labels='', show_minLoss=True)

Bases: object

Losses of training set and validataion set of steps after burn-in phase, which are used to plot losses in PlotPosterior

panel(data)

panel_data(index)

panels_data()

class colfi.plotter.PlotHparamsEffect(fiducial_params, chain_mcmc=None, randn_nums=0.123, path='ann')

Bases: object

get_bestFits_ann()

get_devs(bins=10, smooth=1, show_hist=True)

load_chains()

load_subLine_point_chains(path, randn_num)

panel_data(bins=10, smooth=1, show_hist=True)

plot(bins=10, smooth=1, show_hist=True, save_fig=True, save_path='figures')

points2line(chains, values, nde_type)

property xlabel

class colfi.plotter.PlotMultiPosterior(chains, param_names, path='ann', nde_types=['ANN', 'MDN'], randn_nums=[1.123, 1.123], params_dict=None)

Bases: PosteriorInfo

Plot posterior distribution for multiple ANN chains.

Parameters:

• chains (list) – The ANN chains obtained after burn-in phase.

• param_names (list) – A list which contains the parameter names, e.g. [‘H0’,’ombh2’,’omch2’].

• path (str, optional) – The path of the results saved. Default: ‘ann’

• nde_types (list, optional) – A list that contains names of NDEs. Default: [‘ANN’,’MDN’]

• randn_nums (list, optional) – A list that contains random number which identifies the saved results. Default: [1.123,1.123]

• params_dict (dict or None, optional) – Information of cosmological parameters that include the labels, the minimum values, and the maximum values. See params_dict_zoo(). Default: None

Variables:

• chain_true_path (str, optional) – The path of the true chain of the posterior which can be obtained by using other methods, such as the MCMC method. Note: only .npy and .txt file is supported. Default: ‘’

• label_true (str, optional) – The legend label of the true chain. Default: ‘True’

• fiducial_params (list, optional) – A list that contains the fiducial cosmological parameters. Default: []

• show_idx (None or list, optional) – The index of cosmological parameters when plotting contours. This allows us to change the order of the cosmological parameters. If None, the order of parameters follows that in the ANN chain. If list, the minimum value of it should be 1. See PlotPosterior. Default: None

• file_identity_str (str, optional) – A string that identifies the files saved to the disk, which is useful to identify the saved files. Default: ‘’

property contour_name

get_contours(bins=100, smooth=3, fill_contours=False, sigma=2, show_titles=True, line_width=2, lims=None, legend=True, save_fig=True)

class colfi.plotter.PlotPosterior(chain_all, chain, param_names, path='ann', nde_type_pair=['ANN', 'MNN'], randn_num=1.234, burnInEnd_step=None, params_dict=None, good_losses=None)

Bases: PosteriorInfo

Plot posterior distribution using the ANN chains.

Parameters:

• chain_all (list) – The ANN chains obtained in all steps.

• chain (array-like) – The good ANN chain obtained after burn-in phase.

• param_names (list) – A list which contains the parameter names, e.g. [‘H0’,’ombh2’,’omch2’].

• path (str, optional) – The path of the results saved. Default: ‘ann’

• nde_type_pair (list, optional) – A list that contains two NDEs, the first NDE is used to estimate parameters in the burn-in phase, the second NDE is used to estimate parameters after the burn-in phase. Therefore, the first NDE is ued to find the burn-in end step and the second NDE is used to obtain the posterior. Default: [‘ANN’,’MNN’]

• randn_num (float or str, optional) – A random number that identifies the saved results. Default: 1.234

• burnInEnd_step (None or int, optional) – The burn-in end step. Default: None

• params_dict (dict or None, optional) – Information of cosmological parameters that include the labels, the minimum values, and the maximum values. See params_dict_zoo(). Default: None

• good_losses (None or list, optional) – The losses of training set and validation set after the burn-in phase. Default: None

Variables:

• chain_true_path (str, optional) – The path of the true chain of the posterior which can be obtained by using other methods, such as the MCMC method. Note: only .npy and .txt file is supported. Default: ‘’

• label_true (str, optional) – The legend label of the true chain. Default: ‘True’

• fiducial_params (list, optional) – A list that contains the fiducial cosmological parameters. Default: []

• show_idx (None or list, optional) – The index of cosmological parameters when plotting contours. This allows us to change the order of the cosmological parameters. If None, the order of parameters follows that in the ANN chain. If list, the minimum value of it should be 1. See PlotPosterior. Default: None

• file_identity_str (str, optional) – A string that identifies the files saved to the disk, which is useful to identify the saved files. Default: ‘’

get_contour(bins=100, smooth=3, fill_contours=False, sigma=2, show_titles=True, line_width=2, lims=None, legend=True, save_fig=True)

get_losses(alpha=0.6, show_minLoss=True, layout_adjust=[0.25, 0.25], save_fig=True)

get_steps(show_initParams=False, layout_adjust=[0.3, 0.25], suptitle='', save_fig=True)

class colfi.plotter.PlotPrediction(params_testSet, predParams_testSet, param_names, params_trainingSet=None, predParams_trainingSet=None, params_dict=None, show_reErr=True, randn_num='', path='ann', nde_type='ANN', dataSet_type='testSet', coef_type='R2')

Bases: object

Plot predicted cosmological parameters.

Parameters:

• params_testSet (array-like) – Cosmological parameters in the test set.

• predParams_testSet (array-like) – Predicted cosmological parameters for the test set.

• param_names (list) – A list which contains the parameter names, e.g. [‘H0’,’ombh2’,’omch2’].

• params_trainingSet (array-like, optional) – Cosmological parameters in the training set. Default: None

• predParams_trainingSet (array-like, optional) – Predicted cosmological parameters for the training set. Default: None

• params_dict (dict or None, optional) – Information of cosmological parameters that include the labels, the minimum values, and the maximum values. See params_dict_zoo(). Default: None

• show_reErr (bool, optional) – If True, will calculate and show the best-fit values and standard deviations of the relative errors between the predicted parameters and the true parameters. Default: True

• randn_num (float or str, optional) – A random number that identifies the saved results. Default: ‘’

• path (str, optional) – The path of the results saved. Default: ‘ann’

• nde_type (str, optional) – A string that indicate which NDE should be used. See NDEs. Default: ‘ANN’

• dataSet_type (str, optional) – The type of the data set. Default: ‘testSet’

• coef_type (str, optional) – A quantity that quantifies the degree of linear correlation, which can be Pearson correlation coefficient (‘r’) or coefficient of determination (‘R2’). Default: ‘R2’

• show_idx (None or list, optional) – The index of cosmological parameters when plotting figures. This allows us to change the order of the cosmological parameters. If None, the order of parameters follows that in the training data. If list, the minimum value of it should be 1. See PlotPosterior. Default: None

• file_identity_str (str, optional) – A string that identifies the files saved to the disk, which is useful to identify the saved files. Default: ‘’

Return type:

None.

plot(lat_n=3, panel_size=(4, 3), layout_adjust=[0.3, 0.25], suptitle='')

save_fig()

class colfi.plotter.PosteriorInfo(param_names, path='ann', params_dict=None)

Bases: object

Some information of NDEs, cosmological parameters, and chains, which will be used in PlotPosterior

property chain_true

load_ndeInfo(randn_num)

property param_labels

colfi.plotter.R2(obs, pred)

Coefficient of determination https://en.wikipedia.org/wiki/Coefficient_of_determination https://baike.baidu.com/item/%E5%8F%AF%E5%86%B3%E7%B3%BB%E6%95%B0/8020809?fromtitle=coefficient%20of%20determination&fromid=18081717&fr=aladdin https://doi.org/10.1093/mnras/stz010

obs: observed data pred: predicted data

colfi.plotter.pcc(x, y)

Pearson correlation coefficient https://en.wikipedia.org/wiki/Pearson_correlation_coefficient